Borrowing it
Nothing to install: this file belongs to gsbakshi/nebula-tv. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/gsbakshi/nebula-tv/main/.claude/skills/install/SKILL.mdgit clone --depth 1 https://github.com/gsbakshi/nebula-tvWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/gsbakshi/nebula-tv/install)<a href="https://agentmods.dev/skills/gsbakshi/nebula-tv/install"><img src="https://agentmods.dev/badge/skills/gsbakshi/nebula-tv/install/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/gsbakshi/nebula-tv/install"><img src="https://agentmods.dev/badge/skills/gsbakshi/nebula-tv/install.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00030 | $0.00347 |
| Opus 5 | $0.00015 | $0.00173 |
| Sonnet 5 | $0.00006 | $0.00069 |
| Haiku 4.5 | $0.00003 | $0.00035 |
Grade A, and why
install scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Build and install to a connected Android TV:
./gradlew installDebug
Before reporting results, handle these cases:
No Device Found
If the install fails with "no connected devices":
-
Network ADB (most common for TV):
adb connect <tv-ip-address>:5555Find TV IP: Android TV Settings → Network → Your network → Advanced → IP address Then enable: Settings → Device Preferences → Developer options → Network debugging
-
USB ADB: Connect USB cable → Developer options → USB debugging → Allow this computer
-
Verify:
adb devicesshould list the TV
Successful Install
Report:
- Device name and Android version
- APK size installed
- How to launch directly:
adb shell am start -n com.example.nebula/.MainActivity - Note: To use as actual launcher, go to TV Settings → Apps → Default apps → Home app → Nebula
Multiple Devices
If multiple devices connected, installDebug installs to all. To target one:
./gradlew installDebug -Pandroid.targetDeviceSerialNumber=<serial>
Get serial from: adb devices
After Install: Quick Sanity Checks
Suggest these quick verifications after install:
- App grid loads (PackageManager query working)
- Navigation bar D-pad works (left/right between tabs)
- App launches when pressing OK on an app card
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 46 lines · 30 tokens per session scan A 7f0af89a7ca3
install is a skill published in the GitHub repository gsbakshi/nebula-tv (5 stars, last pushed 6mo ago), licensed MPL-2.0. It adds 30 tokens to every session and 347 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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